Handicapping NCAA Football Totals: Profitable Over/Under Systems

A lot of college football bettors focus on NCAAF point spreads and ignore totals. That doesn’t make a lot of sense since in a lot of cases the numbers are softer in NCAA than the NFL.

First, some basics. It’s important to shop around for the best number when handicapping college football. Having multiple online sports books makes it easy to find games that have a point or more difference. You can exploit these discrepancies to your advantage.

The numbers are going to be higher in NCAA than they are in the pros. There is a lot more scoring as the defenses aren’t nearly as good. Here’s a look at the key numbers for betting college totals. The average pro number is now around 45.7 while in college you’ll see it average out to 57.

Top College Football Over/Under Betting Systems for Totals

So how to do most people handicap totals? The most basic strategy is to look at the scoring averages for both teams. Where the public falls short is they tend to focus on the offensive numbers for both teams. They simply add up the two scoring averages and compare that number to the total set by the books.

I’m going to caution against such simplistic handicapping. You’ll quickly learn that the most obvious bet is typically a losing one and I have plenty of data to back it up.

A more true indicator would be combining how many points a team is scoring and allowing. For example, if a team is averaging 24.4 ppg and allowing 18.6 ppg. The average combined score in all their contests to this point is 43. You would figure out this number for the other side, add the two together and divide by 2.

Best NCAAF Total Systems for UNDER

Two NCAA Football Teams Scoring Average Higher Than the Total

When two schools are averaging more than the number is set at, most novice bettors want to hammer the over. I ran the numbers on two teams averaging more combined points than the total.

Wins

Losses

Push

Win %

739

536

19

58.0%

The results were very clear, and with a 1,205 game sample set there is a lot of data backing up a play on the under.

Now, let’s see if we can’t tighten it up. Let’s look at how the records panned out when the squads averaged different points more than the mark.

PPG Over Line

Wins

Losses

Win %

>0

523

357

59.4%

>1

382

254

60.1%

>2

255

174

59.4%

>3

178

123

59.1%

My next thought was to see if the time of the year mattered. I figured that during the early part of the season team’s averages might be a little off due to a small sample size and playing inferior/superior opponents. So these are all the situations where both schools are averaging more than the total.

Week

Wins

Losses

Win %

2

48

52

48.0%

3

59

51

53.6%

4

52

26

66.7%

5

57

43

57.0%

6

57

44

56.4%

7

63

36

63.6%

8

63

42

60.0%

9

61

48

56.0%

10

54

47

53.5%

11

57

38

60.0%

12

72

38

65.5%

13

61

45

57.5%

14

29

21

58.0%

Total

600

414

59.2%

I was spot on with avoiding this system early, though Week 2 is the only week of the season where this system isn’t blindly profitable. Things really start to get good in Week 4. If you want to see what games fall under this system during the season, just clink on this link.

Strong Winds = Lower Scoring Games

One of the reasons the line could be set lower than the scoring averages is the weather. Everyone thinks snow and rain as the adverse conditions that hinder offenses from scoring, but in reality what I have found is the wind has the biggest effect.

Here are how the different average wind speeds relate to going under the number.

Wind Speed

Wins

Losses

ROI

0

227

245

-6.3%

1

146

137

0.4%

2

285

300

-5.4%

3

430

468

-6.9%

4

528

573

-6.7%

5

489

525

-5.9%

6

477

457

-0.5%

7

417

431

-4.1%

8

330

306

1.2%

9

283

249

3.8%

10

228

193

5.6%

11

155

167

-5.9%

12

142

133

0.7%

13

112

76

15.8%

14

83

63

11%

15

73

53

12.4%

16

39

42

-6%

17

31

21

15.4%

18

32

29

2.4%

19

22

14

19.7%

20+

57

34

22.9%

Things started turning at the 8+ mph mark. Here are the results.

Wind Speed

Wins

Losses

ROI

8+

1587

1380

4.3%

13+

449

332

12%

Basically when the wind starts blowing 13+ MPH I’m going to take a strong look at the UNDER. If I like an OVER I’m likely going to throw it out if the wind is blowing 8+ MPH.

Both Schools Recently Going OVER the Total By Heavy Margins

There is a recency bias in betting. The general public looks at squads trending one way and thinks it’s going to continue. So it stands to reason that when two teams meet who have been playing over in their recent games you might want to zig to the public’s zag and take the under. Here’s the situation I looked up.

Two teams who have gone 30+ points over the combined last three totals. The UNDER has been pretty profitable in this spot.

Wins

Losses

Win %

71

55

56.3%

These results are based on the cumulative score in regards to the total. For example, Team 1 goes under in their first game by 2. Then in the following two weeks they exceed the mark by 35. Their combined margin is over 30 points, thus making the under the smart play.

These numbers are also based on the game totals, not the individual team’s score. A game set at 42 may end with a 48-14 score. Let’s say Team 1 was on the losing end. That game still went over the posted number by 20. This is a preferred situation for playing the under. One of the teams involved did not contribute to the excessive scoring.

It’s a relatively small sample size, but stands up to reason so they are definitely teams I’ll consider in the future. Here is the system if you ever want to check it out.

Profitable NCAA Football Strategy for OVER

Teams Scoring Less Than Their High Total

We know how strong the under is when teams are averaging more than the total. What about when teams are averaging less. I used 52 points or more as the baseline. A staggering data pool of 1,212 games returned. Of those the over cashed 55.8 percent of the time, a 649-541-22 (54.5%) record. The average final score was 62.2. This is almost 10 points higher than the baseline.

A Full Point Less Than a High Total

I decided to tighten up the system to see if I could end having to lay so much action/juice each week. I changed the scoring average to a full three point less than the total. I also raised the baseline to 63 points or more. That eliminated a lot, but still returned a large amount of data. The over/under record in this scenario was 150-111-4. This increased the win percentage on over bets to 56%.

Of course, if you have a local book that has no idea of what you are doing you can sometimes parlay correlated sides with totals. You can make a killing by swinging the odds to your favor on these.